Diffusion Coefficient Values Research Articles

Prostate Cancer Risk Prediction Model Using Clinical and Magnetic Resonance Imaging–Related Findings: Impact of Combining Lesions' Locations and Apparent Diffusion Coefficient Values

Objectives The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)–related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance. Methods This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa. Seven clinical, 3 patient-level MRI-related, and 4 lesion-level MRI-related findings were extracted. After feature selection, 2 logistic regression models with and without lesions-level findings were created using data from facility I and II (development cohort). The area under the receiver operating characteristic curve (AUC) between the 2 models was compared in the PI-RADS ≥3 population in the development cohort and Facility III (validation cohort) using the Delong test. Interfacility differences of the selected predictive variables were evaluated using the Kruskal-Wallis test or chi-squared test. Results Selected lesion-level features included the peripheral zone involvement and apparent diffusion coefficient (ADC) values. The model with lesions-level findings had significantly higher AUC than the model without in 655 examinations in the development cohort (0.81 vs 0.79, respectively, P = 0.005), but not in 553 examinations in the validation cohort (0.77 vs 0.76, respectively). Large interfacility differences were seen in the ADC distribution (P < 0.001) and csPCa proportion in PI-RADS 3–5 (P < 0.001). Conclusions Adding lesions-level findings improved the csPCa discrimination in the development but not the validation cohort. Interfacility differences impeded model generalization, including the distribution of reported ADC values and PI-RADS score-level csPCa proportion.

The Added Value of Apparent Diffusion Coefficient and Histogram Analysis in Assessing Treatment Response of Locally Advanced Cervical Cancer

Objective The aim of the study is to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging in assessing treatment response in cervical cancer patients. Methods A retrospective analysis was done for 50 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy and underwent magnetic resonance imaging and diffusion-weighted imaging. Treatment response was classified into 4 categories according to RECIST criteria 6 months after therapy completion. Apparent diffusion coefficient (ADC) values were measured using both region of interest (ROI) ADC and whole lesion (WL) ADC histogram for all cases at both baseline pretreatment and posttreatment Magnetic resonance imaging studies. Changes in ADC values were calculated and compared between groups. Results The percentage change of ROI-ADCmean at a cutoff value of >20 had excellent discrimination of responders versus nonresponders, while the percentage change of WL-ADCmean, ADCmin, and ADCmax at cutoff values of >12.5, >35.8, and > 19.6 had acceptable discrimination of responders versus nonresponders. Logistic regression analysis revealed that only baseline WL ADCmin was a statistically significant independent predictor of response. Cancer cervix patients with baseline ADCmin < or equal to 0.73 have 12.1 times higher odds of exhibiting a response. Conclusions The percentage change of ROI-ADCmean and WL histogram ADCmean values after concurrent chemoradiotherapy can predict response. Pretreatment WL histogram ADCmin was a statistically significant independent predictor of posttherapy response.

Application of functional magnetic resonance imaging to evaluate renal structure and function in type 2 cardiorenal syndrome.

There is a lack of diagnostic non-invasive imaging technology for assessing the early structural and functional changes of the kidney in type 2 cardiorenal (CRS) patients. This study aims to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for clinical application in type 2 CRS patients, to provide imaging markers for the assessment of kidney damage. This is a retrospective observational clinical study conducted in Nanjing, China. The clinical characteristics, including age, gender, medical history, laboratory results, and ultrasound and magnetic resonance imaging results were collected from the electronic medical record. Thirty-one patients with type 2 CRS, 20 patients with chronic heart failure (HF) and 20 healthy controls were enrolled and divided into type 2 CRS, HF and control groups. All the participants underwent magnetic resonance imaging (MRI) scanning. The apparent diffusion coefficient (ADC) value and IVIM-DWI parameters including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were obtained. The correlation between estimated glomerular filtration rate (eGFR), renal size and imaging parameters was evaluated by Spearman correlation analysis. ADC and D of the renal cortex in patients with type 2 CRS were lower than those in the healthy control group. ADC and f in the HF group were lower than those in the control group. D was positively correlated with the length (r = 0.3752, P = 0.0013) and transverse diameter (r = 0.3258, P = 0.0056) of the kidney. ADC (r = 0.2964, P = 0.0121) and D (r = 0.3051, P = 0.0097) were positively correlated with eGFR. Renal cortical ADC and D values could distinguish type 2 CRS patients from the healthy controls with area under the curve (AUC) of 0.723 and 0.706, respectively. The ADC and D values were not only correlated with renal function, but also had lower levels in type 2 CRS. The IVIM-DWI parameter D was also related to kidney size, but further research is needed to determine whether it can be used as a novel imaging marker for type 2 CRS.

Unsupervised clustering analysis-based characterization of spatial profiles of inaccuracy in apparent diffusion coefficient values with varying acquisition plan orientation and diffusion weighting gradient direction - A large multicenter phantom study.

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis.&#xD;A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s/mm2) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function (ar2+br+c) . Moreover, for each Δ-profile, the maximum minus minimum of the fitting function (δmax) was calculated. &#xD;The parametersa,b,c, andδmaxshowed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parameter a (p< 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system.&#xD;The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction.

Prediction of genetic profile of breast carcinoma on MRI using a combination of DCE‐MRI, DWI, and MR spectroscopy: A prospective observational study

AbstractBackgroundClassification of breast cancer based on gene expression has emerged as the standard approach in its management, owing to the distinct prognoses and treatment responses observed among different subtypes. The aim of this study was to prospectively assess the imaging features of the molecular subtypes of breast cancer using multiparametric magnetic resonance imaging (mMRI) with the combined assessment of dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI), diffusion‐weighted imaging (DWI), and MR spectroscopy (MRS).MethodsThis was a prospective observational single‐center cohort study, which included women with BI‐RADS 4−5 lesions on mammography/ultrasound (US) who subsequently underwent 1.5 T MRI (encompassing DCE‐MRI, DWI, and MRS). The histological subtypes of breast cancer were assessed. Estrogen receptor (ER), progesterone receptor (PR), Ki‐67 status, and human epidermal growth receptor‐2 (HER2) expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: luminal A, luminal B, HER2‐enriched (Her2en), and triple‐negative breast carcinoma (TNBC). Statistical associations between the four molecular subtypes and MRI features were investigated.ResultsFifty patients were included in the study. Circumscribed margins were significantly correlated with triple‐negative tumors compared to others (78% versus 6%, p &lt; 0.001). Spiculated margins were observed in non‐triple negative tumors. Rim enhancement was significantly correlated to triple‐negative tumors compared to all other subtypes (71.4% versus 25%, p = 0.035). Mean apparent diffusion coefficient (ADC) values were significantly lower for luminal subtypes compared to non‐luminal subtypes (p &lt; 0.001). The total choline (tCho) signal‐to‐noise ratio (SNR) was higher in triple‐negative tumors. A combined algorithm using DCE‐MRI, DWI, and MRS can predict TNBC and Her2en with specificity of 86.6% and 100%, respectively, and sensitivity of 100% and 85.37%, respectively.ConclusionThe combination of mMRI with DCE‐MRI, DWI, and MRS can accurately differentiate the molecular subtypes of breast carcinoma.

Abstract Sa1101: Association Between Changes in Hypoxic-Ischemic Brain Injury and Neurological Outcomes in Cardiac Arrest Survivors.

Aim: Quantitative analysis of hypoxic-ischemic brain injury (HIBI) using apparent diffusion coefficient (ADC) values from brain magnetic resonance imaging (MRI) is used to predict poor neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors. Our study aimed to determine the association between changes in HIBI and neurological outcomes in OHCA survivors using two types of MRI. Methods: This observational study used data collected prospectively from a tertiary academic hospital. The study included adult comatose OHCA survivors who underwent targeted temperature management (TTM) and brain MRI within 6 hours (first) and 72–96 hours (second) using the same MR scanner. We analyzed the neurological outcomes based on brain volumes below a specific ADC value using 10-step intervals within the range of 200–1200 × 10 -6 mm 2 /s for the first and second MRIs. ADC-R(x) represents the proportion of total brain volume occupied by voxels, with ADC values ranging from 200 × 10 -6 mm 2 /s to each threshold. Additionally, ADC(x) represents the proportion of total brain volume occupied by voxels that are “threshold - 10” at each threshold. Poor neurological outcomes were categorized as cerebral performance levels 3–5, observed 6 months after OHCA. Results: Overall, 122 patients (25% female) were included in the analysis, of whom 61 (50%) showed poor neurological outcomes. Higher ADC-R(x) were observed in the poor neurological outcome group on both the first and second MRI scans at all ADC intervals (all P &lt; 0.05) (Figure 1). In the Good Neurological Outcome group, the ADC-R values ranged between 370 and 940, with the second MRI value being smaller than that of the first (right shift, P &lt; 0.05). In contrast, the Poor Neurological Outcome group showed ADC-R values ranging between 340 and 1190, with the second MRI value being higher than that of the first (left shift, P &lt; 0.05) (Figure 2). The difference in ADC(x) between the second and first MRI based on neurological outcome showed a positive change in ADC below 620 for a poor neurological outcome and a positive change in ADC above 740 for a good neurological outcome (Figure 3). Conclusions: In this cohort study, we observed that the initial HIBI that occurred during OHCA worsened or returned to normal depending on the neurological outcome after TTM. Prospective multicenter studies are required to generalize our findings.

Diffusion-Weighted Imaging Does Not Differentiate Between Bacterial and Fungal Skull Base Osteomyelitis.

Apparent diffusion coefficient (ADC) value helps in differentiating infections from neoplasms on magnetic resonance imaging (MRI). We investigate the diffusion-weighted images in skull base osteomyelitis (SBO) to evaluate if ADC values can differentiate fungal and bacterial SBO and to analyse the microbiology of all SBO patients. Retrospective observational study. Quaternary care referral centre. A retrospective review of 142 patients diagnosed and treated for SBO patients from January 2010 to May 2023 was done. Chi-square or Fisher's exact test was used to compare ADC values of bacterial and fungal SBO. The most common pathogens isolated were Pseudomonas (42.2%), Aspergillus (30.98%), and S. aureus (23.94%). The average ADC value of affected soft tissues among patients was 1.13 ± 0.26 × 10-3 mm2/s compared to the average ADC value of normal soft tissue, 1.34 ± 0.31 × 10-3 mm2/s. There was no statistical significance when comparing the average ADC values of bacterial and fungal SBO patients (p value = 0.142). This study suggests that though infection due to Pseudomonas was the commonest, it was detected only in 42.2% of patients. More than half of the cases had organisms other than Pseudomonas, demanding the clinician to obtain deeper biopsies early in the course of the disease for microbiological analysis. DWI does not help differentiate bacterial and fungal SBO, again emphasising the need for deeper tissue biopsies in all these patients to assist in the early identification of the pathogen.

Glucose chemical exchange saturation transfer MRI for predicting the histological grade of rectal cancer: a comparative study with amide proton transfer-weighted and diffusion-weighted imaging.

To evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer. This prospective analysis included 60 patients with preoperative rectal cancer who underwent pelvic glucoCEST, amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI). In total, 21 low-grade and 39 high-grade cases were confirmed by postoperative pathology. The MTRasym (1.2 ppm), MTRasym (3.5 ppm), and apparent diffusion coefficient (ADC) values of lesions between the low-grade and high-grade groups were compared. The area under the receiver operating characteristic curve (AUC) was generated to evaluate the diagnostic performance of each technique. Logistic regression (LR) analysis was applied to determine independent predictors and for multi-parameter combined diagnosis. Elevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all p < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC for differentiating between low- and high-grade rectal cancer cases were 0.792, 0.839, and 0.855, respectively. The diagnostic performance of the combination of the three indexes was improved (AUC, 0.969; sensitivity, 95.24%; specificity, 87.18%). The good consistency and reliability of the combination of independent predictors were demonstrated by calibration curve analysis and DCA. The glucoCEST MRI without contrast injection, APTWI, and DWI all facilitate the assessment of histological grade in rectal cancer, and the combination of the three can effectively discriminate between high- and low-grade rectal cancer, which is expected to be a promising imaging marker. The glucose chemical exchange saturation transfer MRI method facilitates the assessment of histological grade in rectal cancer and offers additional information to improve the diagnostic performance of amide proton transfer-weighted imaging, and diffusion-weighted imaging. Glucose chemical exchange saturation transfer imaging could differentiate histological grade. Amide proton transfer-weighted and diffusion-weighted were associated with histological grade. The combination of different parameters showed the best diagnostic performance.

Quantitative Estimation of Iron and Fat Content in Prostate Cancer by Multiparametric MRI and Its Application in Optimizing D'Amico Score.

The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI). To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients. Prospective. Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years). 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence. The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected. Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant. Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723). This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved. 2 TECHNICAL EFFICACY: Stage 2.

Numerical Simulations for Calibration Setup for Dynamic Contrast-Enhanced Ultrasonography Imaging Protocol.

This study presents an investigation of an innovative microfluidic flow separator using both numerical and experimental approaches to calibrate contrast-enhanced ultrasound scanners. Numerical simulations were conducted using Lagrangian particles tracking and passive scalar transport methodologies using the OpenFOAM software. The experimental validation confirmed the accuracy of the numerical simulations, particularly at an imposed total pressure of , showing an excellent agreement in particle distributions. The study emphasizes the computational efficiency and modeling of passive scalar transport, providing valuable understanding into the behavior of scalar quantities in microfluidic systems. An optimized diffusion coefficient value of was identified, showing its critical role in achieving accurate simulation results and optimizing the performance of microfluidic flow separators for contrast-enhanced ultrasound scanner calibration.

Impact of calcium and copper co-doping on the oxygen transport of layered nickelates: a case study of Pr1.6Ca0.4Ni1–yCuyO4+δ and a comparative analysis

This study aims at investigating oxygen content and diffusion in the Pr1.6Ca0.4Ni1‑yCuyO4+δ (y = 0.0–0.4) series. Single-phased materials were obtained via nitrate combustion using glycerol as a fuel. The materials exhibit an orthorhombic structure, which correlates well with the absolute oxygen content in the samples in the range of 4.15–3.97. The oxygen mobility in the samples was investigated by a temperature-programmed isotope exchange of oxygen (TPIE) with C18O2 in a flow reactor. The obtained curves, comprising few extrema, were fitted using a mathematical model that includes two or three distinct oxygen forms, each characterized by a specific diffusion coefficient value. The oxygen tracer diffusion coefficient values and the contribution of fast oxygen forms tend to decrease with increasing Cu content (except for Pr1.6Ca0.4Ni0.8Cu0.2O4+δ), which can be explained by the variation of highly mobile interstitial oxygen content since the effective activation energy values remains the same within the calculation error. Comparative analysis shows that this behavior is similar to the trends observed and discussed for Ca- and Cu-doped La and Nd nickelates. Nevertheless, the highest D* value for Ofast was demonstrated for the Pr1.6Ca0.4Ni0.8Cu0.2O4+δ sample (9.4·10–8 cm2/s at 700 °C), which correlates well with superior electrochemical properties of the electrodes on its basis.

Complexation of Trivalent Lanthanides with Hexaalkyl Nitrilotriacetamides into Methylimidazolium-Based Ionic Liquids: Spectroscopic, Electrochemical, Calorimetric, and Theoretical Insights.

Exothermic, spontaneous inner-sphere complexation of trivalent lanthanides with N,N,N',N',N″,N″-hexaalkyl-substituted nitrilotriacetamides (HRNTAs) into 1-hexyl-3-methylimidazolium-bis(trifluoromethylsulfonyl)imide (C6mim NTf2) is reported with a predominant presence of ML and ML2 complexes having complexation constants, β1Eu 3.10 ± 0.02 ,β1Nd 2.67 ± 0.03 and β2Eu 5.22 ± 0.04, β2Nd 4.95 ± 0.02, respectively, for the n-butyl-substituted HRNTA (HBNTA); while those for the n-hexyl derivative (HHNTA) are β1Eu 4.27 ± 0.03, β1Nd 3.71 ± 0.03 and β2Eu 7.70 ± 0.03, β2Nd 7.18 ± 0.04, respectively. HHNTA shows better complexing ability; whereas the stronger complexation of the Nd3+ over Eu3+ is attributed to the lanthanide contraction. Furthermore, the nature of the ionic liquid also influences the extent of complexation with the trend: C4mim NTf2 > C6mim NTf2 > C8mim NTf2, which follows the order of their dielectric constants. Judd-Ofelt parameters were calculated from photoluminescence data to get an idea about the symmetry of the Eu3+ complexes. Electrochemical investigations give diffusion coefficient values of 1.17 × 10-7 and 8.26 × 10-8 cm2/s for the Eu3+ complexes of HBNTA and HHNTA, respectively. Changes in the spectral characteristics and peak positions are evidenced in the FTIR spectra on the complexation of Eu3+ with the HRNTA ligands in C6mim NTf2. Structure optimization for the complexes was performed by DFT computations.

Diagnostic Utility of Diffusion-Weighted Imaging in Distinguishing Common Pediatric Posterior Fossa Tumors: A Single Center Retrospective Study.

Pediatric posterior fossa tumors pose diagnostic challenges due to their diverse histopathological features and variable clinical presentations. Conventional magnetic resonance imaging (MRI) serves as the initial diagnostic tool; however, additional modalities, such as diffusion-weighted imaging (DWI), are essential for refining tumor classification. This retrospective single-center study aimed to evaluate the diagnostic utility of apparent diffusion coefficient (ADC) parameters in distinguishing between the most common pediatric posterior fossa tumors. Fifty-nine patients under the age of 18 (27 females and 32 males) with histopathologically diagnosed primary posterior fossa tumors underwent pre-treatment conventional and diffusion MRI. Apparent diffusion coefficient values were measured from solid tumor regions and normal cerebellar parenchyma, with subsequent calculation of tumor/normal cerebellar ADC ratios. The median ADC values for pilocytic astrocytomas (PAs) were 1786.2 × 10-6 mm2 /s, ependymomas 1144.9 × 10-6 mm2 /s, and for medulloblastomas 666.1 × 10-6 mm2 /s were significantly different (P < .001 for all three). Similarly, the median ADC ratios demonstrated discriminatory potential, with PAs showing the highest ratio (2.46), followed by ependymomas (1.55) and medulloblastomas (0.89) (P < .001 for all three). Receiver operating characteristic analysis revealed distinct ADC cutoffs and ratios for differentiating all tumor types from each other. Despite limitations, such as a small cohort size and different MRI protocols, our results show that ADC metrics are especially useful for distinguishing between the most common pediatric posterior fossa tumors. We recommend that future studies integrate advanced imaging techniques and larger cohorts to improve diagnostic accuracy.

Assessment of soil gas radon migration and transport through the estimation of radon diffusion length and diffusion coefficient in the soil matrix

Naturally occurring radioactive gas, radon is a decay product of radium present in the soil. Radon flux reaches the atmosphere through exhalation from soil surface. This study aims to estimate the radon diffusion length and diffusion coefficient in soil matrix on the premises of HNB Garhwal University campus at Tehri Garhwal, India. Soil gas radon concentration was measured at different depths (15, 30, 45, 60, 75, and 90 cm) using a Smart RnDuo (Scintillation-detector) at 14 different locations in the university campus. Simultaneous measurement of surface and mass exhalation rates was also carried out for each location. Gamma-ray spectrometry was used to estimate the radium content in soil samples. Results of the present study suggest that soil gas radon concentration increases with increasing sampling depth. The diffusion coefficient and length were calculated using the soil gas radon concentration gradient within the soil matrix. The average value of diffusion length (l) and diffusion coefficient (D) were obtained as, ls = 0.70 ± 0.22 m, Ds = 0.0054 ± 0.0049 m2 sec-1 and la = 0.77 ± 0.63 m, Da = 0.0073 ± 0.014 m2 sec-1 by Fick’s diffusion model and analytic models, respectively.

Assessment and integration of multiparametric MRI for liver fibrosis staging in rat non-alcoholic steatohepatitis: Evaluation of diagnostic efficiency and model interpretation

ObjectivesMulti-parametric MRI techniques, including intravoxel incoherence motion (IVIM), iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ), T2* mapping and T2 mapping, were employed to develop and validate a predictive model for non-alcoholic steatohepatitis (NASH) diagnosis and liver fibrosis (LF) staging in rats. The combined model was interpreted using SHapley Additive exPlanations (SHAP) values for model understanding. Materials and methods160 healthy Sprague-Dawley (SD) rats were divided into control (n = 24) and experimental (n = 136) groups, and the 12-week and 16-week groups were injected intraperitoneally with carbon tetrachloride (CCl4) for 4 weeks, one month before the final feeding period. All rats were subjected to pathological examination to determine LF stage. Upon the study’s completion, 147 SD rats were assessed for liver fibrosis. Results84 SD rats were diagnosed with NASH and 31, 10, and 43 rats were histologically diagnosed with no fibrosis (F0), early LF (F1-F2), and late LF (F3-F4). For diagnosis of NASH and staging of liver fibrosis associated with NASH, a combined multi-parameter magnetic resonance imaging prediction model has a higher area under the ROC curve (AUC) than individual diffusion parameters, especially in advanced stages of fibrosis, with an AUC of 0.929 for the combined model. In SHAP, the free fluid(FF) value contributes most to the model for diagnosing NASH and advanced liver fibrosis, while the T2 value contributes most for diagnosing liver fibrosis and the apparent diffusion coefficient (ADC) value contributes most for diagnosing liver cirrhosis. ConclusionsThe integration of several magnetic resonance imaging methods along with SHAP value analysis provides a more comprehensive and theoretical investigation into the mechanisms and factors contributing to the progression of liver fibrosis in NASH, offering insights from various angles.

Synergistic effect of NASICON Na3V2(PO4)2F3 and 2D MXene for high-performance symmetric Sodium-ion batteries

Sodium fluorophosphate-based Na3V2(PO4)2F3 (NVPF) cathode materials have been widely analyzed in Sodium-ion batteries (SIB) owing to their high energy density and high working voltage. However, the low electronic conductivity of NVPF is a factor hindering their efficient use. To enhance the electronic conductivity of NVPF, in this work, a porous Na3V2(PO4)2F3 and a 2D Ti3C2-based MXene nanocomposite was synthesized using a facile sol-gel method. The NVPF, with the presence of two active redox couples, is a suitable choice for symmetric batteries. The NVPF + 2D MXene nanocomposite was analyzed for its structural and thermal characteristics, and a symmetric cell prepared from them was investigated for its electrochemical characteristics. Structural analysis of the materials developed indicates that the MXene addition has not altered the crystal structure of the NVPF. A remarkable improvement in the electrochemical performance of NVPF in the sodium symmetric cell is noticed, as indicated by its high specific discharge capacity of 92mAhg-1 at 1C for the MXene-incorporated composite structures. This improvement in electrochemical behaviour is confirmed in the rate capability curves, GCD curves, and GITT curves. The diffusion coefficient values obtained from GITT analysis showed improved kinetics in the synthesized material due to the MXene incorporation. The calculated values of the diffusion coefficient of Na+confirms the accelerated kinetics of Na+ ion migration during the intercalation/de-intercalation process in the MXene 5wt% nanocomposites, with a value of 9.57 × 10–9 cm2s-1 when compared to 4. 14 × 10–9 cm2s-1 for the pristine sample.

Role of Apparent Diffusion Coefficient Value and Apparent Diffusion Coefficient Ratio as Prognostic Factors for Prostate Cancer Aggressiveness.

Prostate cancer is one of the most prevalent cancers in the male population. To determine the aggressiveness of suspected lesions precisely, predictive models are increasingly being developed using quantitative MRI measurements, and particularly the ADC value. This study aimed to determine whether ADC values could be used to establish the aggressiveness of prostate cancer. A retrospective single-center study included 398 patients with prostate cancer who underwent a multiparametric MRI prior to radical prostatectomy. DWI ADC values were measured (µm2/s) using b values of 50 and 1000. The dominant lesion best visualized on MRI was analyzed. The ADC values of the index lesion and reference tissue were compared to tumor aggressivity according to the Gleason grade groups based on radical prostatectomy results. Statistical analysis was performed using the Mann-Whitney U test, Kruskal-Wallis H test, Spearman's rank correlation, and ROC curves. A very strong negative correlation (rs = -0.846, p < 0.001) between ADC and GS was found. ROC analysis revealed an AUC of 0.958 and an ADC threshold value of 758 µm2/s in clinically significant prostate cancer diagnoses using the absolute ADC value, with no advantage of using the ADC ratio over the absolute ADC value being identified. DWI ADC values and the calculated ADC ratio have a significant inverse correlation with GS. The findings indicate a strong capability in determining prostate cancer aggressiveness, as well as the possibility of assisting with assigning PI-RADS categories using ADC as quantitative metrics.

The strength and hydraulic conductivity characteristics of sewage sludge ash and natural soil mixtures – a sustainable alternate landfill liner material

ABSTRACT This paper examines the feasibility of the natural soil and sewage sludge ash (SSA) mixtures, which satisfy the criteria to be used as landfill liners. The effect of SSA content on hydraulic conductivity and strength characteristics of natural soil and SSA mixtures has been investigated through a series of laboratory tests. The results demonstrate that mixtures exhibit an increase in both hydrodynamic diffusion coefficient and strength with the increasing SSA content. With the content of SSA from 0% to 5%, the values of the hydrodynamic diffusion coefficient ( D ) ranged from 3.5 × 10−10 to 15 × 10−10 cm2/s. The increase in the hydrodynamic diffusion coefficient is minor for low SSA content and significant for SSA content exceeding 5%. The inclusion of 5% SSA content results in a hydrodynamic diffusion coefficient that is approximately three times higher than that observed in natural soil. The results were obtained from soil triaxial tests, revealing that the mixtures containing SSA exhibited a significant increase in both the initial tangent modulus and the ultimate principal stress difference compared to those of natural soil. The SSA content with the highest value exhibits maximum initial tangent modulus and ultimate principal stress. The comprehensive analysis of the strength and hydraulic diffusion conductivity of the mixtures demonstrates that the incorporation of 3% SSA results in a significant enhancement in strength, while marginally increasing hydrodynamic diffusion coefficients. Therefore, it can be inferred that the utilization of mixtures containing 3% SSA content as a liner material is suitable.

Predictive value of metabolic parameters and apparent diffusion coefficient derived from 18F-FDG PET/MR in patients with non-small cell lung cancer

BackgroundMultiple models intravoxel incoherent motion (IVIM) based 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance(18F-FDG PET/MR) could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored.ObjectiveTo compare the value of 18F-FDG PET/MR metabolic parameters and diffusion parameters in the prognostic assessment of patients with NSCLC.Meterial and methodsChest PET and IVIM scans were performed on 61 NSCLC patients using PET/MR. The maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), diffusion coefficient (D), perfusion fraction (f), pseudo diffusion coefficient (D*) and apparent diffusion coefficient (ADC) were calculated. The impact of SUVmax, MTV, TLG, D, f, D*and ADC on survival was measured in terms of the hazard ratio (HR) effect size. Overall survival time (OS) and progression-free survival time (PFS) were evaluated with the Kaplan–Meier and Cox proportional hazard models. Log-rank test was used to analyze the differences in parameters between groups.Results61 NSCLC patients had an overall median OS of 18 months (14.75, 22.85) and a median PFS of 17 months (12.00, 21.75). Univariate analysis showed that pathological subtype, TNM stage, surgery, SUVmax, MTV, TLG, D, D* and ADC were both influential factors for OS and PFS in NSCLC patients. Multifactorial analysis showed that MTV, D* and ADC were independent predicting factors for OS and PFS in NSCLC patients.ConclusionMTV, D* and ADC are independent predicting factors affecting OS and PFS in NSCLC patients. 18F-FDG PET/MR-derived metabolic parameters and diffusion parameters have clinical value for prognostic assessment of NSCLC patients.

Discrimination Model Construction for Non-Lactational Mastitis and Breast Cancer Based on Imaging Features.

Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. Methods This study is a retrospective analysis. Clinical data of 108 patients suspected of NLM based on imaging diagnosis, admitted to The First Affiliated Hospital with Nanjing Medical University between May 2018 and August 2023, were collected. Among them, 94 cases were pathologically confirmed as NLM and 14 cases as BC. Univariate and multivariate logistic regression analyses were performed on the patients' clinical data, ultrasound features, and MRI features to select the risk factors for discriminating NLM and BC, and construct a discrimination model. The discrimination performance of the model was analyzed with the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results In the NLM group, there were 24 cases of granulomatous lobular mastitis (25.53%) and 70 cases of plasma cell mastitis (74.47%). In the BC group, there were 2 cases of infiltrating ductal carcinoma, 2 cases of atypical hyperplasia, 3 cases of papillary carcinoma, and 7 cases of ductal carcinoma in situ. Age, internal blood flow, calcification, edge, enhancement characteristics, apparent diffusion coefficient (ADC) values, and time-intensity curve (TIC) type were independent factors for differentiating NLM and BC (p < 0.05). The ROC analysis showed that the area under the curve of the model for discriminating NLM and BC was 0.920. The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.